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import numpy as np import matplotlib.pyplot as plt sm = 52.2 # å¹³åïŒæ¯å¹³åïŒ ss = 9.5 # æšæºåå·®ïŒæ¯æšæºåå·®ïŒ sn = 1000 # æ¯æ° x = np.random.normal(loc=sm, scale=ss, size=sn) plt.hist(x) plt.show()
çªå· | å€ | çªå· | å€ | çªå· | å€ | çªå· | å€ | çªå· | å€ |
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1 | 65.22 | 2 | 51.41 | 3 | 53.83 | 4 | 54.94 | 5 | 50.95 |
6 | 60.98 | 7 | 40.49 | 8 | 51.59 | 9 | 45.87 | 10 | 41.89 |
11 | 48.59 | 12 | 60.96 | 13 | 61.29 | 14 | 68.36 | 15 | 44.00 |
16 | 27.13 | 17 | 44.82 | 18 | 48.64 | 19 | 60.19 | 20 | 52.94 |
21 | 58.39 | 22 | 49.19 | 23 | 65.86 | 24 | 54.13 | 25 | 48.13 |
26 | 64.79 | 27 | 71.59 | 28 | 43.65 | 29 | 57.86 | 30 | 55.06 |
31 | 46.60 | 32 | 62.65 | 33 | 54.67 | 34 | 64.94 | 35 | 58.89 |
36 | 48.25 | 37 | 54.10 | 38 | 58.68 | 39 | 45.52 | 40 | 51.02 |
41 | 48.80 | 42 | 57.48 | 43 | 47.65 | 44 | 60.85 | 45 | 60.13 |
46 | 55.48 | 47 | 50.86 | 48 | 47.52 | 49 | 54.38 | 50 | 48.81 |
51 | 47.48 | 52 | 46.58 | 53 | 48.13 | 54 | 58.52 | 55 | 53.22 |
56 | 63.26 | 57 | 52.76 | 58 | 39.00 | 59 | 50.62 | 60 | 62.60 |
61 | 59.12 | 62 | 38.90 | 63 | 41.00 | 64 | 54.03 | 65 | 61.37 |
66 | 70.12 | 67 | 47.42 | 68 | 52.52 | 69 | 60.35 | 70 | 56.44 |
71 | 61.90 | 72 | 45.62 | 73 | 37.24 | 74 | 68.67 | 75 | 61.15 |
76 | 51.95 | 77 | 50.58 | 78 | 40.71 | 79 | 66.60 | 80 | 46.50 |
81 | 66.00 | 82 | 58.42 | 83 | 45.36 | 84 | 55.10 | 85 | 30.56 |
86 | 50.63 | 87 | 39.55 | 88 | 44.97 | 89 | 57.44 | 90 | 53.20 |
91 | 59.54 | 92 | 46.96 | 93 | 58.90 | 94 | 62.14 | 95 | 52.78 |
96 | 41.06 | 97 | 54.95 | 98 | 60.30 | 99 | 60.38 | 100 | 43.09 |
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æšæºåå·® | æ¯æšæºåå·®=8.58 | æšæ¬æšæºåå·®=6.56 |
忣(Variance) | æ¯åæ£=73.65 | äžå忣=42.98 |
å差平æ¹å | =5,315.65 | =623.74 |
æšæ¬æšæºåå·®ã¯ãæ¯æšæºå差㮠äžåæšå®éã§ã¯ãªãããæ¯æšæºåå·®ã®æšå®ã¯ã è¿äŒŒçã«æšæ¬æšæºåå·®ã§è¡ãããšãå€ã 14 ) 15 ) ã
import numpy as np import matplotlib.pyplot as plt import random sm = 52.2 # å¹³åïŒæ¯å¹³åïŒ ss = 9.5 # æšæºåå·®ïŒæ¯æšæºåå·®ïŒ sn = 10000 # æ¯æ° en = 5 # æšæ¬æ° x = np.random.normal(loc=sm, scale=ss, size=sn) sampled = random.sample(x.tolist(), en) #ç¡äœçºæœåº fig = plt.figure() ax1 = fig.add_subplot(2, 1, 1) ax2 = fig.add_subplot(2, 1, 2) ax1.hist(x) ax2.hist(sampled) plt.show() average1 = np.mean(x) stdev1 = np.std(x) average2 = np.mean(sampled) stdev2 = np.std(sampled) print('inf',sm,ss) print(sn,average1,stdev1) print(en,average2,stdev2)
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